Ultra-small, Built for AI Breakthroughs

ASUS Ascent GX10 AI Supercomputer

Based on NVIDIA DGX™ Spark

ASUS Ascent GX10 AI Supercomputer

Revolutionary AI Performance on Your Desktop

The groundbreaking ASUS Ascent GX10 AI Supercomputer, powered by the state-of-the-art NVIDIA® GB10 Grace Blackwell Superchip found in the NVIDIA DGX Spark, brings petaflop-scale AI computing capabilities directly to the desks of developers, AI researchers, and data scientists. This innovative device is designed to empower local AI development with its exceptional performance and advanced features.

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Unparalleled AI Performance

Up to 1,000 AI TOPS of Processing Power
Delivers exceptional AI performance, enabling seamless handling of large AI workloads.

128GB of Coherent Unified System Memory
Empowers model development, experimentation, and inferencing with ample memory capacity.

  • Up To 1,000 AI TOPS Processing Power
  • 128G Coherent Unified System Memory

Cutting-Edge Architecture

NVIDIA GB10 Grace Blackwell Superchip
Advanced chip features a robust Blackwell GPU with fifth generation Tensor Cores and FP4 support.

High-Performance 20-Core Arm CPU
Enhances data preprocessing and orchestration, accelerating model tuning and real-time inferencing.

NVLink®-C2C Technology
Provides a cohesive CPU+GPU memory model with five times the bandwidth of PCIe 5.0.

    Handles Large Parameter Gen AI Models

    Support for AI Models 200 Billion Parameters
    Prototype, fine-tune, and infer the latest AI reasoning models directly on your desktop.

    Integrated NVIDIA® ConnectX®-7 Network Technology
    Link two ASUS Ascent GX10 systems to handle even larger models, such as Llama 3.1 with 405 billion parameters.

      Local Development, Scalable Deployment

      Seamless Transition to Cloud
      Leverage NVIDIA AI platform software architecture to move models from desktop environments to DGX Cloud or any accelerated cloud or data center infrastructure with minimal code adjustments.

      Cost-Effective Experimentation Platform
      Free up essential compute resources in clusters better suited for training and deploying production models.

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